Multimedia recommendation aims to predict user preferences where users interact with multimodal items. Collaborative filtering based on graph convolutional networks manifests impressive performance gains in multimedia recommendation. This is attributed to the capability of learning good user and item embeddings by aggregating the collaborative signals from high-order neighbors. However, previous researches [37,38] fail to explicitly mine different behavior patterns (i.e., item categories, common user interests) by exploiting user-item and item-item graphs simultaneously, which plays an important role in modeling user preferences. And it is the lack of different behavior pattern constraints and multimodal feature reconciliations that results in performance degradation. Towards this end, We propose a Hybrid Clustering Graph Convolutional Network (HCGCN) for multimedia recommendation. We perform high-order graph convolutions inside user-item clusters and item-item clusters to capture various user behavior patterns. Meanwhile, we design corresponding clustering losses to enhance user-item preference feedback and multimodal representation learning constraint to adjust the modality importance, making more accurate recommendations. Experimental results on three real-world multimedia datasets not only demonstrate the significant improvement of our model over the state-of-the-art methods, but also validate the effectiveness of integrating hybrid user behavior patterns for multimedia recommendation.

In this paper, we propose a novel graph learning framework for phrase grounding in the image. Developing from the sequential to the dense graph model, existing works capture coarse-grained context but fail to distinguish the diversity of context among phrases and image regions. In contrast, we pay special attention to different motifs implied in the context of the scene graph and devise the disentangled graph network to integrate the motif-aware contextual information into representations. Besides, we adopt interventional strategies at the feature and the structure levels to consolidate and generalize representations. Finally, the cross-modal attention network is utilized to fuse intra-modal features, where each phrase can be computed similarity with regions to select the best-grounded one. We validate the efficiency of disentangled and interventional graph network (DIGN) through a series of ablation studies, and our model achieves state-of-the-art performance on Flickr30K Entities and ReferIt Game benchmarks.